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We present a new method to obtain spatio-temporal information from aggregateddata of stationary traffic detectors, the ``adaptive smoothing method''. Inessential, a nonlinear spatio-temporal lowpass filter is applied to the inputdetector data. This filter exploits the fact that, in congested traffic,perturbations travel upstream at a constant speed, while in free traffic,information propagates downstream. As a result, one obtains velocity, flow, orother traffic variables as smooth functions of space and time. Applicationsinclude traffic-state visualization, reconstruction of traffic situations fromincomplete information, fast identification of traffic breakdowns (e.g., inincident detection), and experimental verification of traffic models.  We apply the adaptive smoothing method to observed congestion patterns onseveral German freeways. It manages to make sense out of data whereconventional visualization techniques fail. By ignoring up to 65% of thedetectors and applying the method to the reduced data set, we show that theresults are robust. The method works well if the distances between neighbouringdetector cross sections do not exceed 3 km.

An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information